An Improved Rapidly‐Exploring Approach to Off‐Road Path Planning by Leveraging Dynamic Velocity Constraints and Trajectory Smoothing

ABSTRACT Off‐road path planning and navigation often struggle with complex challenges, such as diverse surface conditions that demand adaptability, stability‐sensitive vehicle dynamics on low‐adhesion terrain, and the persistent trade‐off between real‐time performance and path quality. To address these challenges, an improved rapidly‐exploring random tree (IRRT) algorithm is developed to adjust the dynamic exploration domain considering the vehicle's design speed and local terrain features, which can affect vehicle's operational stability, thereby increasing path feasibility and environmental adaptability. Furthermore, a nonlinear model predictive controller (NMPC) is deployed in the lower layer of the proposed RRT path planning framework, smoothing the generated path and enhancing ride comfort through terrain‐aware adjustments. Both a 100 × 100 meter simulated environment and a real‐world 1:10 scale test site, featuring distinct terrain types, i.e., hard roads, natural terrain, and low hills, with obstacles. The results show that the proposed two‐layer path planning framework, improved RRT algorithm integrating with NMPC, reduces path length by 6.9% and total turning angle by 12.3% compared to RRT, while maintaining a maximum curvature of 0.134 m − 1 (well within the safety limit of 0.2 m − 1 ) and improving ride comfort by 80.4%. On the other hand, although the computation time increases by 272.2%, the resulting gains in path quality and stability justify the trade‐off. The proposed method demonstrates a viable solution for off‐road vehicle navigation across diverse terrains, effectively balancing path feasibility, ride smoothness, and computational efficiency.